257 research outputs found

    ๊ฐœ์ธํ™” ์ฝ”๋”” ์ถ”์ฒœ์„ ์œ„ํ•œ ์œ„์Œ์„ฑ ์ฆ๋ฅ˜ ๋ฐ ๋Œ€์กฐ ํ•™์Šต

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2022.2. ์ด์ƒ๊ตฌ.Personalized outfit recommendation has recently been in the spotlight with the rapid growth of the online fashion industry. However, recommending outfits has two significant challenges that should be addressed. The first challenge is that outfit recommendation often requires a complex and large model that utilizes visual information, incurring huge memory and time costs. One natural way to mitigate this problem is to compress such a cumbersome model with knowledge distillation (KD) techniques that leverage knowledge from a pretrained teacher model. However, it is hard to apply existing KD approaches in recommender systems (RS) to the outfit recommendation because they require the ranking of all possible outfits while the number of outfits grows exponentially to the number of consisting clothing items. Therefore, we propose a new KD framework for outfit recommendation, called False Negative Distillation (FND), which exploits false-negative information from the teacher model while not requiring the ranking of all candidates. The second challenge is that the explosive number of outfit candidates amplifying the data sparsity problem, often leading to poor outfit representation. To tackle this issue, inspired by the recent success of contrastive learning (CL), we introduce a CL framework for outfit representation learning with two proposed data augmentation methods. Quantitative and qualitative experiments on outfit recommendation datasets demonstrate the effectiveness and soundness of our proposed methods.์ตœ๊ทผ ์˜จ๋ผ์ธ ํŒจ์…˜ ์‚ฐ์—…์ด ๊ธ‰์„ฑ์žฅํ•˜๋ฉด์„œ ๊ฐœ์ธํ™” ์ฝ”๋”” ์ถ”์ฒœ์ด ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฝ”๋”” ์ถ”์ฒœ์€ ํ•ด๊ฒฐํ•ด์•ผ ํ•  ๋‘ ๊ฐ€์ง€ ์ค‘์š”ํ•œ ์ฑŒ๋ฆฐ์ง€๊ฐ€ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ฑŒ๋ฆฐ์ง€๋Š” ์ฝ”๋”” ์ถ”์ฒœ์ด ์ฃผ๋กœ ์‹œ๊ฐ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” ๋ณต์žกํ•˜๊ณ  ํฐ ๋ชจ๋ธ์„ ํ•„์š”๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ƒ๋‹นํ•œ ์‹œ๊ฐ„๊ณผ ๋ฉ”๋ชจ๋ฆฌ ๋น„์šฉ์ด ๋ฐœ์ƒํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๋Š” ํ•œ ๊ฐ€์ง€ ์ž์—ฐ์Šค๋Ÿฌ์šด ๋ฐฉ๋ฒ•์€ ์‚ฌ์ „ ํ›ˆ๋ จ๋œ ๊ต์‚ฌ ๋ชจ๋ธ์˜ ์ง€์‹์„ ํ™œ์šฉํ•˜๋Š” ์ง€์‹ ์ฆ๋ฅ˜ ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์ด๋Ÿฌํ•œ ์„ฑ๊ฐ€์‹  ๋ชจ๋ธ์„ ์••์ถ•ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ถ”์ฒœ ์‹œ์Šคํ…œ์˜ ๊ธฐ์กด ์ง€์‹ ์ฆ๋ฅ˜ ์ ‘๊ทผ๋ฒ•์€ ๊ฐ€๋Šฅํ•œ ๋ชจ๋“  ์ฝ”๋””์˜ ์ˆœ์œ„๋ฅผ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ์ฝ”๋””์˜ ์ˆ˜๋Š” ๊ตฌ์„ฑ๋˜๋Š” ์˜์ƒ์˜ ์ˆ˜์— ๋”ฐ๋ผ ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ฝ”๋”” ์ถ”์ฒœ์— ๊ธฐ์กด ์ง€์‹ ์ฆ๋ฅ˜ ์ ‘๊ทผ๋ฒ•์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ์ƒ๋‹นํžˆ ๊นŒ๋‹ค๋กœ์šด ์ž‘์—…์ด๋‹ค. ๋”ฐ๋ผ์„œ ์šฐ๋ฆฌ๋Š” ๋ชจ๋“  ํ›„๋ณด ์ฝ”๋””์˜ ์ˆœ์œ„๋ฅผ ์š”๊ตฌํ•˜์ง€ ์•Š์œผ๋ฉด์„œ ๊ต์‚ฌ ๋ชจ๋ธ์˜ ์œ„์Œ์„ฑ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” ์œ„์Œ์„ฑ ์ฆ๋ฅ˜๋ผ๋Š” ์ƒˆ๋กœ์šด ์ง€์‹ ์ฆ๋ฅ˜ ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ฑŒ๋ฆฐ์ง€๋Š” ์ฝ”๋”” ํ›„๋ณด์˜ ํญ๋ฐœ์ ์ธ ์ˆ˜๋กœ ์ธํ•ด ๋ฐ์ดํ„ฐ ํฌ์†Œ์„ฑ ๋ฌธ์ œ๊ฐ€ ์ฆํญ๋˜์–ด ์ข…์ข… ์ฝ”๋”” ํ‘œํ˜„(representation)์ด ์ข‹์ง€ ์•Š๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ตœ๊ทผ ๋Œ€์กฐ ํ•™์Šต์˜ ์„ฑ๊ณต์— ์˜๊ฐ์„ ๋ฐ›์•„ ์ƒˆ๋กœ์šด ๋‘ ๊ฐ€์ง€ ๋ฐ์ดํ„ฐ ์ฆ๊ฐ• ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜๋Š” ์ฝ”๋”” ํ‘œํ˜„ ํ•™์Šต์„ ์œ„ํ•œ ๋Œ€์กฐ ํ•™์Šต ํ”„๋ ˆ์ž„์›Œํฌ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์ฝ”๋”” ์ถ”์ฒœ ๋ฐ์ดํ„ฐ ์„ธํŠธ์— ๋Œ€ํ•œ ์–‘์  ๋ฐ ์งˆ์  ์‹คํ—˜์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์˜ ํšจ๊ณผ์™€ ํƒ€๋‹น์„ฑ์„ ๋ณด์ธ๋‹ค.Abstract i Contents ii List of Tables v List of Figures vi 1 Introduction 1 2 Related Work 5 2.1 Outfit Recommendation 5 2.2 Knowledge Distillation 6 2.3 Contrastive Learning 6 3 Approach 7 3.1 Background: Computing the Preference Score to an Outfit 8 3.1.1 Set Transformer 9 3.1.2 Preference score prediction 10 3.2 False Negative Distillation 10 3.2.1 Teacher model 10 3.2.2 Student model 11 3.3 Contrastive Learning for Outfits 13 3.3.1 Erase 14 3.3.2 Replace 14 3.4 Final Objective: FND-CL 14 3.5 Profiling Cold Starters 15 3.5.1 Average (avg) 16 3.5.2 Weighted Average (w-avg) 16 4 Experiment 17 4.1 Experimental Design 17 4.1.1 Datasets 17 4.1.2 Evaluation metrics 18 4.1.3 Considered methods 18 4.1.4 Implementation details 19 4.2 Performance Comparison 20 4.3 Performance on Cold Starters 21 4.4 Performance on Hard Negative Outfits 22 4.5 Performance with Different ฮฑ 23 4.6 Performance with Different Augmentations 24 4.7 Performance with Different Model Sizes 25 4.8 Performance with Different Batch Sizes 27 4.9 Visualization of the User-Outfit Space 28 5 Conclusion 30 Bibliography 31 A Appendix 37 A.1 Enhancing the Performance of a Teacher Model 37 A.1.1 Teacher-CL 38 A.1.2 Employing Teacher-CL: FND-CL* 39 Abstract (In Korean) 40์„

    ์‚ฌ๋žŒ ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค (SARS-CoV-2)์™€ ๋ผ์ง€ ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค (PEDV)์˜ ์œ ์ „ํ•™์  ๋ถ„์„๊ณผ ์œ ์ „์  ๋ณ€์ด๊ฐ€ ๋ฐ”์ด๋Ÿฌ์Šค ํ•ญ์›์„ฑ๊ณผ ์ง„๋‹จ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฐ€๋Šฅ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2021. 2. ๋ฐ•์šฉํ˜ธ.๋ฐ”์ด๋Ÿฌ์Šค๋Š” ๋ฉด์—ญ ํšŒํ”ผ๋ฅผ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์ „์  ๋Œ์—ฐ๋ณ€์ด๋ฅผ ํ†ตํ•ด ์ˆ™์ฃผ ๋ฉด์—ญ๊ณผ ๊ณ„์†ํ•ด์„œ ์‹ธ์šฐ๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋ฐ”์ด๋Ÿฌ์Šค์˜ ์ƒ์กด ์ „๋žต์€ ์•ž์œผ๋กœ๋„ ๊ณ„์† ๋  ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ์œ ์ „์ž ๋ณ€์ด์— ๋”ฐ๋ฅธ ์•„๋ฏธ๋…ธ์‚ฐ ์„œ์—ด์˜ ๋น„์ƒ๋™์„ฑ (non-synonymous) ๋ณ€ํ™”๋Š” ๋ฐ”์ด๋Ÿฌ์Šค ์—ํ”ผํ†ฑ์˜ ํ•ญ์›์„ฑ์„ ๋ณ€ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ๊ธฐ์กด์— ๊ฐœ๋ฐœ๋œ ๋ฐฑ์‹ ์˜ ๋ฐฉ์–ด๋Šฅ์„ ์ €ํ•˜์‹œํ‚ฌ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๋ฐ”์ด๋Ÿฌ์Šค์˜ ์—ผ๊ธฐ์„œ์—ด์˜ ๋ณ€์ด๋Š” ํ˜„์žฌ ์ผ์ƒ์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์ง„๋‹จ ๊ธฐ์ˆ ์ธ ์ค‘ํ•ฉํšจ์†Œ์—ฐ์‡„๋ฐ˜์‘ (PCR)๊ณผ ํšจ์†Œ๊ฒฐํ•ฉ๋ฉด์—ญํก์ฐฉ๋ถ„์„๋ฒ• (ELISA)์˜ ์ง„๋‹จ ์ •ํ™•๋„๋ฅผ ์ €ํ•ด ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ฐ”์ด๋Ÿฌ์Šค๊ฐ€ ์ง„ํ™”ํ•จ์— ๋”ฐ๋ฅธ ์ค‘์š”ํ•œ ๊ทธ๋“ค์˜ ์œ ์ „ํ•™์  ๋ณ€ํ™”๋ฅผ ์กฐ์‚ฌํ•˜๊ณ  ์ถ”์ ํ•˜๋Š” ๊ฒƒ์€ ๋ฐ”์ด๋Ÿฌ์Šค์— ๋Œ€ํ•œ ์ ์ ˆํ•œ ์˜ˆ๋ฐฉ ๋ฐ ์ง„๋‹จ ์ „๋žต์„ ์ˆ˜๋ฆฝํ•˜๋Š” ๋ฐ ๋งค์šฐ ํฐ ๋„์›€์ด ๋œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜„์žฌ ์‚ฌ๋žŒ๊ณผ ๋ผ์ง€์—์„œ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ๋Š” ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค์˜ ์œ ์ „์  ๋ณ€์ด์™€ ๊ทธ ๋ณ€์ด๋“ค์ด ๋ฐ”์ด๋Ÿฌ์Šค์˜ ํ•ญ์›์„ฑ๊ณผ ์ง„๋‹จ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฐ€๋Šฅ์„ฑ์— ๋Œ€ํ•ด ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ์ตœ๊ทผ ์‚ฌ๋žŒ์—์„œ ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ๋Š” severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค์˜ ์ŠคํŒŒ์ดํฌ (S) ๋‹จ๋ฐฑ์งˆ์€ ๋ฐ”์ด๋Ÿฌ์Šค์˜ ์„ธํฌ ๋‚ด ์œ ์ž…์— ๊ฒฐ์ •์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ํ‘œ๋ฉด ๋‹จ๋ฐฑ์งˆ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ํ•ญ์›์„ฑ๊ณผ ๋ฉด์—ญํ•™์  ํŠน์ง•์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด SARS-CoV-2์˜ S ์œ ์ „์ž๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. S ์œ ์ „์ž๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๊ณ„ํ†ตํ•™์  ๋ถ„์„์—์„œ SARS-CoV-2 ๋ถ„๋ฆฌ์ฃผ๋“ค ์‚ฌ์ด์— ๋‘ ๊ฐœ์˜ ์œ ์ „์ž ๊ทธ๋ฃน์ด ์กด์žฌํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด ๋‘ ๊ฐœ์˜ ์œ ์ „์ž ๊ทธ๋ฃน์€ ํ•˜๋‚˜์˜ ํŠน์ด์ ์ธ ์—ผ๊ธฐ์„œ์—ด ๋ณ€์ด์ธ D614G์— ์˜ํ•ด ๋‚˜๋‰˜์—ˆ๋‹ค. ์ด ๋ณ€์ด๋Š” SARS-CoV-2๊ฐ€ ์ˆ™์ฃผ์˜ ๋ฉด์—ญ์ฒด๊ณ„๋ฅผ ํšŒํ”ผํ•˜๋Š”๋ฐ ๊ฒฐ์ •์ ์ธ ์—ญํ• ์„ ํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋˜์—ˆ๋‹ค. D614G ์—ผ๊ธฐ์„œ์—ด ๋ณ€์ด๋ฅผ ํฌํ•จํ•˜๋Š” S1 domain์˜ ์—ํ”ผํ†ฑ ๋ถ€์œ„์— ๋Œ€ํ•ด ํ•ญ์› ์ง€์ˆ˜ ๋ถ„์„์„ ์‹œํ–‰ํ•œ ๊ฒฐ๊ณผ, SARS-CoV-2b ์œ ์ „์ž ๊ทธ๋ฃน์ด SARS-CoV-2a ์œ ์ „์ž ๊ทธ๋ฃน์— ๋น„ํ•ด ์œ ์˜์ ์œผ๋กœ ๊ฐ์†Œํ•œ ํ•ญ์› ์ง€์ˆ˜๋ฅผ ๋ณด์ด๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, ์ด ์œ ์ „์  ๋ณ€์ด์— ์˜ํ•ด ๋‘ ์œ ์ „์ž ๊ทธ๋ฃน๊ฐ„์˜ ํ•ญ์›์„ฑ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜์˜€์„ ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋˜์—ˆ๋‹ค. ๋‘ ์œ ์ „์ž ๊ทธ๋ฃน๊ฐ„ ํ•ญ์›์„ฑ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค๋ฉด ๋‘ ์œ ์ „์ž ๊ทธ๋ฃน์„ ๋ฐฑ์‹ ์— ํฌํ•จ์‹œํ‚ค๋Š” ๊ฒƒ์ด COVID-19์„ ๋ฐฉ์–ดํ•˜๋Š”๋ฐ ๋ณด๋‹ค ํšจ์œจ์ ์ผ ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ, ์‹ค์ œ๋กœ ๋‘ ์œ ์ „์ž ๊ทธ๋ฃน๊ฐ„ ํ•ญ์›์„ฑ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋Š”์ง€ ํ™•์ธํ•˜๋Š” ๊ฒƒ์ด ์‹œ๊ธ‰ํ•˜๋‹ค. ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ์ „ ์„ธ๊ณ„ ๋ผ์ง€ ์‚ฐ์—…์— ์ง€์†์ ์ด๊ณ  ์‹ฌ๊ฐํ•œ ํ”ผํ•ด๋ฅผ ์ž…ํžˆ๊ณ  ์žˆ๋Š” ๋ผ์ง€์œ ํ–‰์„ฑ์„ค์‚ฌ๋ฐ”์ด๋Ÿฌ์Šค (PEDV)๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ตœ๊ทผ ์–‘๋ˆ์žฅ์˜ PEDV ์œ ๋ณ‘๋ฅ ์€ ์•ฝ 9.92 %๋กœ ์ง€์†์ ์œผ๋กœ ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ์Œ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‰ดํด๋ ˆ์˜ค์บก์‹œ๋“œ (N) ์œ ์ „์ž๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฒ ์ด์ง€์•ˆ ๊ณ„ํ†ต ๋ถ„์„์„ ์ง„ํ–‰ํ•œ ๊ฒฐ๊ณผ, ์„ธ ๊ฐœ์˜ ์ฃผ์š” N ์œ ์ „์ž ๊ธฐ๋ฐ˜ ์œ ์ „์ž ๊ทธ๋ฃน (N1, N2 ๋ฐ N3)๊ณผ ๋‘ ๊ฐœ์˜ ํ•˜์œ„ ์œ ์ „์ž ๊ทธ๋ฃน (N3a๊ณผ N3b) ์„ ํ™•์ธํ•˜์˜€๋‹ค. N ๋‹จ๋ฐฑ์งˆ์— ํฌํ•จ๋œ ์—ํ”ผํ†ฑ ๋ถ€๋ถ„์˜ ํ•ญ์› ์ง€์ˆ˜๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์œ ์ „์ž ๊ทธ๋ฃน๊ฐ„ ํ•ญ์›์„ฑ์— ์ฐจ์ด๊ฐ€ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ฐ•ํ•˜๊ฒŒ ์˜์‹ฌ๋˜์—ˆ๋‹ค. ์—ํ”ผํ†ฑ ๋ถ€์œ„์—์„œ N3 ์œ ์ „์ž ๊ทธ๋ฃน์˜ ํ•ญ์› ์ง€์ˆ˜๋Š” N1 ๋ฐ N2 ์œ ์ „์ž ๊ทธ๋ฃน์˜ ํ•ญ์› ์ง€์ˆ˜์— ๋น„ํ•ด ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” N1 ๋‹จ๋ฐฑ์งˆ์„ ํ•ญ์›์œผ๋กœ ์‚ฌ์šฉํ•˜๋Š” ELISA ํ‚คํŠธ์˜ ์ง„๋‹จ ๊ฒฐ๊ณผ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์ตœ๊ทผ ํ™•์ธ๋œ ํ•œ๊ตญ PED ๋ฐ”์ด๋Ÿฌ์Šค๋“ค์˜ S ์œ ์ „์ž๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ŠคํŒŒ์ดํฌ ๋‹จ๋ฐฑ์งˆ (COE, S1D ๋ฐ 2C10)์˜ B ์„ธํฌ ์—ํ”ผํ† ํ”„ ์„œ์—ด์˜ ์ผ๋ถ€์—์„œ ์œ ์˜์ ์œผ๋กœ ๋‚ฎ์€ ํ•ญ์› ์ง€์ˆ˜๊ฐ€ ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ S ๋ฐ N ์œ ์ „์ž์˜ ๋ฉด์—ญํ•™์  ์ฃผ์š” ๋ถ€์œ„์— ์œ ์ „์  ๋ณ€์ด๊ฐ€ ๋ฐœ์ƒํ•œ PED ๋ฐ”์ด๋Ÿฌ์Šค๋“ค์€ ๊ธฐ์กด์— ํ™•๋ฆฝ๋œ ์ˆ™์ฃผ ๋ฉด์—ญ์„ ํšŒํ”ผํ•˜์—ฌ ๋ผ์ง€ ๋†์žฅ์— ์‹ฌ๊ฐํ•œ ์†์ƒ์„ ์ค„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ง€์†์ ์ธ ๊ฐ์‹œ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๋ฐ”์ด๋Ÿฌ์Šค์˜ ๋ฉด์—ญ ํšŒํ”ผ๋‚˜ ์ง„๋‹จ ์˜ค๋ฅ˜๋ฅผ ์œ ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” ์ค‘์š”ํ•œ ์œ ์ „์  ๋ณ€์ด๋ฅผ ํ˜„์žฌ ์‹ฌ๊ฐํ•œ ๋ฌธ์ œ๊ฐ€ ๋˜๊ณ  ์žˆ๋Š” ์‚ฌ๋žŒ๊ณผ ๋ผ์ง€ ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค์—์„œ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฐœ๊ฒฌ์€ ๋ฐ”์ด๋Ÿฌ์Šค ๊ฐ์—ผ ์˜ˆ๋ฐฉ์— ๋Œ€ํ•œ ๋” ๋‚˜์€ ์ดํ•ด์™€ ๋ณด๋‹ค ์ •ํ™•ํ•œ ์ง„๋‹จ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ๋„์›€์„ ์ค„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค. ๋‚˜์•„๊ฐ€ ํ–ฅํ›„ ์ฝ”๋กœ๋‚˜๋ฐ”์ด๋Ÿฌ์Šค ์ง„ํ™”์— ์ ์ ˆํžˆ ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ์œ ์ „์ž ๋ถ„์„์„ ํ†ตํ•œ ์ง€์†์ ์ธ ๊ฐ์‹œ๊ฐ€ ์œ ์ง€๋˜์–ด์•ผ ํ•œ๋‹ค.Aspect of virus evolution, viruses have continued to fight with host immune through genetic mutation facilitating immune evasion and this strategy of viruses for their survival will continue in the future. Genetic mutation, which may change structural form of viral proteins by non-synonymous changes, may alter antigenicity of a viral epitope and in turn can lead to decreased efficacy of previously developed vaccine for the virus. Furthermore, such genetic mutation can hamper diagnostic accuracy of polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), which are routinely used diagnostic techniques. Thus, it is very important work to investigate and track critical genetic events along with virus evolution. These efforts can give worthy information and insight to establish appropriate prevent and diagnostic strategy for viruses. Herein, human and animal coronaviruses causing sever disease were investigated by genetic and phylogenetic analysis. As stated in chapter I, The S glycoprotein of coronaviruses is important for viral entry and pathogenesis with most variable sequences. Therefore, we analyzed the S gene sequences of SARS-CoV-2 to better understand the antigenicity and immunogenicity of this virus in this study. In phylogenetic analysis, two subtypes (SARS-CoV-2a and -b) were confirmed within SARS-CoV-2 strains. These two subtypes were divided by a novel non-synonymous mutation of D614G. This may play a crucial role in the evolution of SARS-CoV-2 to evade the host immune system. The region containing this mutation point was confirmed as a B-cell epitope located in the S1 domain, and SARS-CoV-2b strains exhibited severe reduced antigenic indexes compared to SARS-CoV-2a in this area. This may allow these two subtypes to have different antigenicity. If the two subtypes have different serological characteristics, a vaccine for both subtypes will be more effective to prevent COVID-19. Thus, further study is urgently required to confirm the antigenicity of these two subtypes. As stated in chapter I, Porcine epidemic diarrhea virus (PEDV) causes continuous, significant damage to the swine industry worldwide. By RT-PCR-based methods, this study demonstrated the ongoing presence of PEDV in pigs of all ages in Korea at the average detection rate of 9.92%. By the application of Bayesian phylogenetic analysis, it was found that the nucleocapsid (N) gene of PEDV could evolve at similar rates to the spike (S) gene at the order of 10โˆ’4 substitutions/site/year. Based on branching patterns of PEDV strains, three main N gene-based genogroups (N1, N2, and N3) and two sub-genogroups (N3a, N3b) were proposed in this study. By analyzing the antigenic index, possible antigenic differences also emerged in both the spike and nucleocapsid proteins between the three genogroups. The antigenic indexes of genogroup N3 strains were significantly lower compared with those of genogroups N1 and N2 strains in the B-cell epitope of the nucleocapsid protein. Indeed, there is different antigenicity between the genogroups based on the N gene, it may affect diagnostic results using commercial ELISA kits based on N1 protein. Similarly, significantly lower antigenic indexes in some parts of the B-cell epitope sequences of the spike protein (COE, S1D, and 2C10) were also identified. PEDV mutants derived from genetic mutations of the S and N genes may cause severe damage to swine farms by evading established host immunities. In conclusion, the crucial genetic variations, which may induce immune evasion or diagnostic error, were revealed in these coronavirus. It is expected that these results provide better understanding for preventing viral infection and more precise diagnosis. Also, constant surveillances through genetic analysis should maintain to appropriately respond to coronavirus evolution in the future.Abstract iv List of Figures ix List of Tables x General introduction 11 Literature review 14 1. Coronavirus 15 1.1. General overveiw 17 1.2. SARS-CoV-2: Human betacoronavirus 17 1.3. Porcine epidemic diarrhea virus: Animal alphacoronavirus 19 2. Virus evolution and genetic analysis on viruses 23 2.1. Antigenic drift and genetic shift 23 2.2. Genetic mutation and recombination 23 2.3. Mutation rate of DNA and RNA viruses 25 2.4. Phenotypic Variation by Mutations 26 2.5. Phylogenetic analysis on viruses 26 3. Impact of genetic mutation on viral antigenicity and diagnosis 28 Chapter I 31 Abstract 32 1. Introduction 33 2. Material and Methods 34 2.1. Sample collection and Phylogenetic analysis 34 2.2. Epitope prediction and antigenic index analysis on S gene sequences 34 3. Results 35 3.1. Phylogenetic analysis on S gene of SARS-CoV-2 35 3.2. Epitope prediction of S protein 35 3.3. Antigenic index analysis on the epitope of S1 subunit 35 4. Discussion 36โ€ƒ Chapter II 43 Abstract 44 1. Introduction 45 2. Materials and Methods 47 2.1. Sample collection, PEDV detection by PCR, and complete sequencing 47 2.2. Genetic analysis of recombination 48 2.3. Bayesian phylogenetic analysis 50 2.4. Pairwise genetic distance (p-Distance) analysis 51 2.5. Inferring ancestral amino acid changes 51 2.6. Amino acids and antigenic index analysis of N gene 51 2.7. Antigenic index analysis of B-cell epitopes in Korean PEDV strains 52 3. RESULTS 53 3.1. The detection of PEDV in Korea from 2017 to 2018 53 3.2. Phylogenetic analysis of global PEDV strains 56 3.3. Evolutionary rates of PEDV genes 60 3.4. Amino acids and antigenic index analysis of N gene sequences 60 3.5. Antigenic index analysis of S protein B-cell epitopes in Korean PEDV strains 61 4. Discussion 66 General conclusions 69 References 71 ๊ตญ๋ฌธ ์ดˆ๋ก 88Docto

    Intra-articular Avulsion Fractures of the Malleolus in Chronic Ankle Pain

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    Purpose: We report our results of arthroscopic treatment of symptomatic avulsion fracture of the malleolus in chronic ankle pain, and also analyzed the clinical and radiological features for evaluating the good candidate for arthroscopic treatment. Material and Methods: Fourteen patients who were diagnosed with intra-articular avulsion fractures of the malleolus received arthroscopic surgery and were followed up for at least a year. The clinical and radiological characters including MRI and arthroscopic findings were reviewed. Clinical assessments were done according to the AOFAS score system. Results: There was a history of inversion type of the injury in most cases and local tenderness of lesion site was a unique. MRI study showed thickened anterior talofibular ligament (ATFL) in 8 cases (57%) and discontinued ATFL in 3 cases (21%). Enhanced signal surrounding soft tissue corresponding to synovial inflammation and impingement was found in 12 cases (86%). Preoperative score of all patients were 74.0ยฑ5.5, which improved to 89.3ยฑ6.7 at the follow-up after the treatment (P<0.001). Conclusions: Most patients had history of injury and localized tenderness in the area coinciding with radiological findings. Thickened ATFL and contrast enhancement around the ossicle were frequently found. Symptomatic avulsion fractures of the malleolus associated with the clinical and radiological findings above could be a good candidate for arthroscopic treatment.ope

    Peroxisome Proliferator-Activated Receptor ฮณ Activation Promotes Adipogenesis in Human Mesenchymal Stem Cells

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    Purpose: In this study, we determined that the troglitazones could induce uniform adipogenesis of human mesenchymal stem cells (MSCs) within a short time in a dose- and a time-dependent manners. Materials and Methods: Human MSCs were isolated from bone marrow and cultured in basal or adipogenic medium in the presence of 0 50 ฮผM troglitazone for 5 days. Then we performed flow cytometry, RT-PCR and western blot analysis. Results: In FACS assay, troglitazone induced adipocyte differentiation in a dose-dependent manner. At concentration of 25 ฮผM troglitazone in adipogenic medium, over 50% of the cells differentiated into adipocytes at day 5. This was accompanied by increased mRNA levels for the adipocyte gene markers (LPL, aP2 and PPARฮณ) in RT-PCR. In western blot analysis, we found that ERK phosphorylation was inhibited in the early stage of adipogenesis. Conclusion: Through the addition of troglitazone as a PPAR ฮณ agonist, we could get the uniform adipogenic differentiation within a short time. Thus, troglitazone directly regulates differentiation of human MSCs into adipocytes; induced PPAR ฮณ expression may play a key regulatory role in this process. And we suggest a role for ERK as a regulatory switch for these differentiation pathwaysope

    ์ค‘์‹ฌ์ฒด ๋‹จ๋ฐฑ์งˆ์ธ CEP215 ์˜ ์„ธํฌ์ฃผ๊ธฐ์— ๋”ฐ๋ฅธ ๊ธฐ๋Šฅ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ƒ๋ช…๊ณผํ•™๋ถ€, 2014. 2. ์ด๊ฑด์ˆ˜.In most animal cells, the centrosome functions as a major microtubule organizing center and controls cellular morphology, migration, subcellular transport and cell division. Understanding the functional mechanisms of this mysterious and interesting organelle at the molecular level has been a great topic in molecular and cellular biology. Meanwhile, proteomic analysis revealed that human centrosome is composed of several hundred kinds of proteins. The composition of the centrosome changes dynamically during the cell cycle. Among the centrosomal proteins, I focused on the functional mechanisms of CEP215, an important pericentriolar material component, for microtubule nucleation and centrosome maturation during interphase and mitosis, respectively. In chapter 1, I investigated the knockdown phenotypes of CEP215 during interphase. It has been reported that CEP215 is involved in several centrosome behaviors such as centrosome cohesion, microtubule nucleation and centrosome maturation. However, the precise mechanisms of these functions have not been thoroughly explored. Thus, I focused on biological roles of CEP215 for microtubule nucleation in interphase. The results revealed that the physical interaction of CEP215 with ฮณ-tubulin is essential for the microtubule nucleation in interphase cells. In chapter 2, I investigated roles of CEP215 in mitotic cells. From chapter 1, I found that CEP215 physically interacts with ฮณ-tubulin in interphase cells. Here, I hypothesized that the interaction of CEP215 with ฮณ-tubulin also contributes to the centrosome maturation during mitosis. It was unexpected that the physical interaction of CEP215 with ฮณ-tubulin is not necessary for the centrosome maturation. Rather, physical interaction of CEP215 with pericentrin is critical for the centrosome maturation and subsequent proper bipolar spindle formation during mitosis. The present results provide an insight into how pericentriolar material components are assembled to form a spindle pole during mitosis.Docto

    Risk factors for medial meniscus posterior root tear

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    BACKGROUND: Medial meniscus posterior root tears (MMPRT) have a different clinical effect from other types of meniscal tears. These tears are very common among Asian people and may be related to the frequent use of postures such as the lotus position or squatting. PURPOSE: The present study was designed to identify the risk factors for MMPRT among an Asian sample. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: An observational study was performed of 476 consecutive patients undergoing an arthroscopic procedure on their medial meniscus from January 2010 to December 2010. One hundred four patients had MMPRT (group 1), and the other patients had other types of medial meniscal tears (group 2). Demographic characteristics (age, sex, body mass index [BMI]), radiographic features (mechanical axis angle, tibia vara angle, tibial slope angle, Kellgren-Lawrence grade [KLG]), and environmental factors (occupation, trauma history, sports activity level, table use or not, bed use or not-variables that are representative of the oriental lifestyle of lotus position and squatting) were surveyed. We assessed the relation of these risk factors to the type of meniscal tear (group 1 or 2). RESULTS: In group 1, there were 7 male and 97 female patients, with an average age of 58.2 years (range, 39-78 years) and BMI of 26.7 ยฑ 3.4 kg/m2. In group 2, there were 136 male and 236 female patients (P < .01 compared with group 1), with an average age of 54.3 years (range, 17-77 years; P < .01) and a BMI of 24.9 ยฑ 3.1 kg/m2 (P < .01). With regard to radiographic features, the mechanical axis angle demonstrated a significantly increased varus alignment in group 1 (4.5ยฐ ยฑ 3.4ยฐ) compared with group 2 (2.4ยฐ ยฑ 2.7ยฐ; P < .01), and the KLG was 1.4 ยฑ 0.8 in group 1 and 0.9 ยฑ 0.6 in group 2 (P < .01). Environmental factors showed no differences in occupation, table use or not, and bed use or not, except sports activity level. There were 41 patients (42.7%) in group 1 and 77 patients (20.6%) in group 2 who did not participate in any recreational activity (P < .01). Multiple logistic regression analysis showed that female sex was associated with a 5.9-fold increase in risk (95% confidence interval [CI], 2.138-16.575), a varus mechanical axis angle with a 3.3-fold increase (95% CI, 1.492-7.153), a BMI more than 30 kg/m2 with a 4.9-fold increase (95% CI, 1.160-20.955), and lower sports activity level with a 2.7-fold increase (95% CI, 1.011-7.163) for MMPRT. CONCLUSION: Persons with MMPRT had significantly increased age, female sex predominance, higher BMI, increased KLG, greater varus mechanical axis angle, and lower sports activity level compared with persons with other types of meniscal tear. After adjusting for other factors, sex, BMI, mechanical axis angle, and lower sports activity level remained strong determinants of MMPRT. Interestingly, oriental postural positions including the lotus position and squatting showed no contribution to increased risk of MMPRT. This suggests that intrinsic risk factors (similar to those that predispose to osteoarthritis) predispose to MMPRT.ope

    Arthroscopic Repair of Type II SLAP Lesion with Bioabsorbable Knotless Suture Anchor: Surgical Technique and Clinical Results

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    Purpose: The purpose of this study was to evaluate the results of bioabsorbable knotless suture anchoring for isolated type II SLAP.0aMaterials and Methods: Fourteen patients with isolated type II SLAP underwent a surgical repair with bioabsorbable knotless anchor arthroscopically. Instability, rotator cuff tears or simple subacromial decompression were excluded. The UCLA and pain of VAS (Visual Analogue Scale), ADL (Activity of Daily Living, from the American Shoulder and Elbow Society) were evaluated and patients underwent a thorough shoulder examination at a minimum follow-up period of 2 years postoperatively.0aResults: At a mean of 27.1 months follow-up. The mean UCLA score improved from 14.4 pre-operatively to 31.2 on last follow-up. The mean VAS for pain was 4.9 and on last follow-up 1.0. The mean VAS for instability was 2.6 and on last follow-up 0.5. The mean ADL was 10.4 and on last follow-up 25.0. 12 patients reported their satisfaction as good to excellent and 10 of the 14 patients returned to their pre-injury level of activity (athletics) (P<0.05).0aConclusion: Arthroscopic repair with bioabsorbable knotless suture anchors is an effective surgical technique for the treatment of an isolated unstable type II SLAP lesion. Overall satisfaction was only 85.7%. 1 patient had severe stiffness and 1 patient had shoulder pain.ope

    Arthroscopic Partial Repair of Massive Contracted Rotator Cuff Tears

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    Typically, massive rotator cuff tears have stiff and retracted tendon with poor muscle quality, in such cases orthopaedic surgeons are confronted with big challenging to restore the cuff to its native footprint. Furthermore, even with some restoration of the footprint, it is related with a high re-tear rate due to less tension free repair and less tendon coverage. In this tough circumstance, the partial repair has yielded satisfactory outcomes at relatively short follow-up by re-creating the transverse force couple of the rotator cuff. Through this partial repair, the massive rotator cuff tear can be converted to the "functional rotator cuff tear" and provide improvement in pain and functional outcomes in patient's shoulder.ope

    Biomechanical evaluation of the influence of posterolateral corner structures on cruciate ligaments forces during simulated gait and squatting

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    Posterolateral corner (PLC) structures of the knee joint comprise complex anatomical soft tissues that support static and dynamic functional movements of the knee. Most previous studies analyzed posterolateral stability in vitro under static loading conditions. This study aimed to evaluate the contributions of the lateral (fibular) collateral ligament (LCL), popliteofibular ligament (PFL), and popliteus tendon (PT) to cruciate ligament forces under simulated dynamic loading conditions by using selective individual resection. We combined medical imaging and motion capture of healthy subjects (four males and one female) to develop subject-specific knee models that simulated the 12 degrees of freedom of tibiofemoral and patellofemoral joint behaviors. These computational models were validated by comparing electromyographic (EMG) data with muscle activation data and were based on previous experimental studies. A rigid multi-body dynamics simulation using a lower extremity musculoskeletal model was performed to incorporate intact and selective resection of ligaments, based on a novel force-dependent kinematics method, during gait (walking) and squatting. Deficiency of the PLC structures resulted in increased loading on the posterior cruciate ligament and anterior cruciate ligament. Among PLC structures, the PT is the most influential on cruciate ligament forces under dynamic loading conditions.ope

    Treatment of Open Proximal Humerus Fracture by Gunshot.

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    PURPOSE: To consider the proper managment of proximal humerus fracture on gunshot wounds. MATERIALS AND METHODS: A 28-year-old male patient, who sustained a gunshot injury on the left arm 5 days ago, was admitted through the emergency department. Although he underwent an emergency surgery (bullet fragment removal and debridement), there remained bullet fragments around the proximal humerus fracture site. The wound seemed to be infected and a partial dehiscence occurred. No neurologic deficit was noted. Immediate exploration and debridement were performed, and an external fixator was applied to restore the anatomical alignment and manage the wounds. Intravenous antibiotics were administered. On the 9th postoperative day, wound debridement was done again, and cement beads mixed with antibiotics were inserted. After two weeks, the external fixator was removed, and the pin sites were closed after debridement. One week later, the open reduction and internal fixation with locking compression plate and screws were done. RESULTS: At 3 months after the internal fixation, the bone union was obtained with satisfactory alignment of the humerus. CONCLUSION: The severity of the soft tissue injury influences the fracture management plan. Further, the risk on lead toxicity should be considered.ope
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